1. Technical Field
The present technology pertains generally to magnetic resonance diagnostic imaging methods and more particularly to methods that simultaneously acquire estimates of the ADC probability density function (ADC PDF) as well as estimate functional connectivity in patients with primary brain tumors using a sequence termed diffusion repeatability evaluation and measurement (DREAM)-MRI, that allows an estimation of uncertainty in ADC quantification by approximating the voxel-wise ADC probability density function (PDF) distribution.
2. Background
Diffusion MRI is an important, yet controversial quantitative cancer imaging biomarker. Diffusion-weighted MRI (or DWI) is a technique that typically attempts to elicit subvoxel information about brain tumor microstructural features. DWI measures of apparent diffusion coefficient (ADC) have been shown to be sensitive to brain tumor cellularity, tumor invasion, the presence of cerebral edema, tissue hypoxia, and the response to therapy. Diffusion characteristics have also been shown to correlate with histopathological grading of gliomas and can be an important predictive and prognostic biomarker for a variety of brain tumor therapies.
The apparent diffusion coefficient (ADC), or magnitude of random water movement within tumors, can be used to estimate tumor cell density, allowing clinicians to monitor and predict treatment response. However, ADC maps/images that are acquired clinically are only an estimate of ADC within the tissue, and therefore are prone to measurement inaccuracies and physiologic noise.
Traditional ADC maps that use a single measurement of apparent diffusion coefficients (ADC) using diffusion MRI do not allow for an estimation of variability in the ADC measurements and may fail to capture the complexity of the tumor microstructure. Clinical MR measures of ADC typically involve a single measurement in three orthogonal directions, then the calculation of the average “isotropic”, or “trace” ADC using information about the level of diffusion weighting (i.e. b-value). Thus, the resulting ADC maps acquired clinically in the art can be considered a single estimate of mean tissue ADC within a voxel. Studies have shown that normal tissue can have variability in ADC estimation of ADC as high as 30% and data from multicenter clinical trials suggest estimation of mean ADC can vary significantly across scanners, field strengths, and acquisition protocols.
Since ADC measurements within brain tumors vary over time and follow highly complex PDF distributions, single estimates of ADC using traditional diffusion MR approaches are often inadequate for characterizing brain tumor tissues. Despite the promising potential, the many sources of variability in ADC measurements exist, resulting in skepticism about the potential widespread applicability of ADC as a reliable and robust imaging biomarker for use in cancer clinical trials.
Accordingly, there is a need for new diffusion MR methods that allows for estimation of uncertainty in ADC quantification by approximating the voxel-wise ADC PDF distribution.